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Name |
Description |
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Total number of attributes. | |
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Total number of discrete valued attributes or fields holding discreting values in the dataset. | |
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Defines the distance model used, when calculating the distance between examples. | |
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Total number of learned examples. | |
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Total number of real valued attributes or fields holding real values in the dataset. | |
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Set to True, if Class indexes are zero based. | |
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Defines the K parameter for the K-NN algorithm. | |
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Set the value of this property to the index of the example that you want to be ignored during the classification. | |
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Specifies the value indicating a "missing value" (no entry) for discrete attributes in the dataset. | |
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Specifies the value indicating a "missing value" (no entry) for real valued attributes in the dataset. | |
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If true, the number of attributes compared will be normalized between comparisons. | |
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Set this property to true, to store all learned examples. |
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